DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Response to Arguments
Rejections Under 35 U.S.C. § 101
Applicant’s arguments, see pg. 9, filed 05/28/2026, with respect to claims 1-20 have been fully considered and are persuasive. The r of claims 1-20 has been withdrawn.
Rejections Under 35 U.S.C. § 102/3
Applicant’s arguments with respect to claims 1-20 have been considered but are moot because the new ground of rejection does not rely on any reference applied in the prior rejection of record for any teaching or matter specifically challenged in the argument.
Claim Objections
Claims 12 and 20 objected to because of the following informalities: It appears that in claims 12 and 20 it was intended to include "the fifth application" in the list of available applications to select from Appropriate correction is required.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claims 1, 4, 5, 6, 7, 8, 11, 14, 16, 17 and 18 are rejected under 35 U.S.C. 103 as being unpatentable over FARROKHABADI US20200126169A1 in view of Rogers U 20210117859A1.
Regarding claims 1 and 14, FARROKHABADI discloses a system to provide an electricity distribution grid platform, comprising: a data processing system comprising one or more processors, coupled with memory, to (¶ 32 Machine learning system 120 may include a digitalprocessor113 that may include one or more digital processing units. Machine learning system 120 also includes memory...);
identify a network connection with a plurality of edge devices located on an electricity distribution grid, wherein each of the plurality of edge devices comprise a processor and memory (see 44 discusses edge systems that are part of an electrical grid in communication with each other. These edge devices are being interpreted as the remote devices. ¶ 79 "Computer system 1000 may further include a communication or network interface 1024. Communication interface 1024 enables computer system 1000 to communicate and interact with any combination of remote devices, remote networks, remote entities, etc." ¶ 32 "Machine learning system 120 may include a digital processor 113 that may include one or more digital processing units. Machine learning system 120 also includes memory...");
identify, based on a configuration associated with the plurality of edge devices, a model configured to perform a function to manage delivery of electricity via the electricity distribution grid (¶ 6 discusses context signatures and how they are used to identify how the device should work in the grid. This is being interpreted as the configuration. ¶ 45" The context manager 510 is in communication with one or more of the edge subsystems 502 by one of feedback or feedforward, by a shared memory or by a communications subsystem (not shown) The context signature may represent the environment of an energy grid. ");
select a model from a plurality of models trained with machine learning based on a local environment attribute associated with a location on the electricity distribution grid at which the plurality of edge devices are located, a model configured to detect photovoltaic electricity generation; (¶6 discusses context signatures and how they are used to identify how the device should work in the grid. This include environmental attributes associated with the location of the device. " ¶ 46 "In an embodiment, the data manager 506 may then forward the ML variables associated with that specific context to the context manager 510... Once the model is created, the model is attached to or associated with its context container and stored in the context container ML", ¶26 “These machine learning models may be used to predict and control energy produced by energy grid systems, such as a photovoltaic”);
provide, for execution on the plurality of edge devices, the application configured with the model to perform the function to manage delivery of electricity via the electricity distribution grid, wherein execution of the application on the plurality of edge devices causes at least one of the plurality of edge devices to generate a control signal to adjust operation of a component of the electricity distribution grid. (¶ 6 and ¶ 46 discuss the context or the configuration of the models/devices and their ability to interact with the electrical grid. ¶ 47 "The models stored in the context container ML repository 512 may be communicated to one or more edge systems 502 by way of feedforward or feedback, shared memory or a communications system (not shown). The machine learning model may then be implemented within the edge subsystem 502 to perform a series of tasks associated with grid 504 through controlling one or more operable elements similar to operable elements 160 that maybe associated with controlling one or more elements of grid 504...").
FARROKHABADI does not disclose expressly an application configured with machine learning models and downloading the application to the edge.
Rogers discloses providing applications configured with machine learning models to edge devices where the models can be deployed. The models are then selected based on the environmental criteria for the most suitable model for the current circumstances. (¶25 “As mentioned, there can be one or more deep learning (DL) models 128, or neural network models, available to the inferencing application 128 which can be stored at the edge server 120…, In at least some embodiments, the model to be used can be determined by consulting a manifest 134 that specifies current conditions or information for the inferencing application,”, ¶ 27 As mentioned, in such an embodiment deep learning models can be separated out from an application and can instead be mounted into an application container 220 at runtime from the host system storage. The host system stores models in local storage 210, with a directory 212A through 212C for each model that can each include one or more subdirectories for different versions of that model. These model directories can be mounted, individually or as part of an overall model directory, into the application container 220 at runtime so the directories are visible to the inferencing application 202, and the inferencing application 202 can begin execution with an initial version of each model, as may be specified by a manifest file 216.)
FARROKHABADI and Rogers are analogous art because they are from the same field of endeavor edge computing, handling applications and machine learning models.
At the time of the invention, it would have been prima facie obvious to one of ordinary skill, in the art as of the effective filing date, to incorporate Rogers into FARROKHABADI to provide an application that contains multiple models for photovoltaic detection that can select a model from the plurality of models responsive to the identification of the application.
The suggestion/motivation for doing so would have been to increase the efficiency of the edge devices by reducing the latency (Rogers ¶20 discloses “Running inferencing applications at edge locations can have various advantages for customers-such as reduced latency-as the edge locations can be closer to the connected media sources and sensors, which can be important for latency-critical applications or services that may be accessed from a variety of different geographic locations.”).
Therefore, it would have been prima facie obvious to one of ordinary skill, in the art as of the effective filing date, to combine FARROKHABADI and Rogers for the benefit of Identifying an application wherein the application comprises at least a fifth application configured to detect photovoltaic electricity generation; select, responsive to identification of the application, to obtain the invention as specified in the claims 1 and 14.
Regarding claim 4 and 16, all limitations of claim 1 and 14 have been discussed above, FARROKHABADI in view of Rogers discloses comprising the data processing system to: identify a second application installed on the plurality of edge devices (FARROKHABADI ¶ 10 discusses identifying a second model based on the configuration explained in 16. ¶ 45 "The context manager 510 is in communication with one or more of the edge subsystems 502 by one of feedback or feedforward, by a shared memory or by a communications subsystem");
provide an instruction to the application to cause the application to output data to the second application to perform one or more functions to manage delivery of electricity via the electricity distribution grid ( FARROKHABADI ¶ 10 discusses how the context signatures are associated with ML models configured to control the grid. " ¶ 44 "an example system 500for implementing a first machine learning model based on the output of a second machine learning model, according to some embodiments. The system 500 includes one or more grid data collection and control edge subsystems 502. The system 500 may include up to n edge subsystems 502 which are in communication with up to n grids 504").
Regarding claim 5 and 17, all limitations of claims 1 and 14 have been discussed above, FARROKHABADI in view of Rogers discloses identify a second application installed on the plurality of edge devices (FARROKHABADI ¶ 10 discusses the context signatures and how they are associated with a second model on the grid. ¶ 45 "The context manager 510 is in communication with one or more of the edge subsystems 502 by one of feedback or feedforward, by a shared memory or by a communications subsystem (not shown)");
update a configuration of the second application based on output from the application (FARROKHABADI ¶ 10 discusses the context signatures and how the output is determined by the configuration and ¶ 11 the output of the first application. ¶31 "In an embodiment, the system 100 also includes a context manager subsystem 110. The Context Manager Subsystem 110 is also configured to, on a periodic basis, load or upgrade one or more machine learning models in one or more of the DCC subsystems 102 based on the signature and quality score").
Regarding claims 7 and 18, all limitations of claims 1 and 14 have been discussed above, FARROKHABADI in view of Rogers discloses comprising: receiving, by the data processing system, a data stream from at least one of the plurality of edge devices (FARROKHABADI ¶ 79 For example, communication interface 1024 may allow computer system 1000 to communicate with remote devices 1028 over communications path 1026, which may be wired and/or wireless, and which may include any combination of LANs, WANs, the Internet, etc. Control logic and/or data may be transmitted to and from computer system 1000 via communication path);
update the model based on the data stream (FARROKHABADI ¶ 31 The Context Manager Subsystem 110 is also configured to, on a periodic basis, load or upgrade one or more machine learning models in one or more of the DCC subsystems 102 based on the signature and quality score);
deploying, by the data processing system, the updated model to the plurality of edge devices for use by the application installed on the plurality of edge devices (FARROKHABADI ¶ 31 the Context Manager Subsystem 110 is also configured to, on a periodic basis, load or upgrade one or more machine learning models in one or more of the DCC subsystems 102 based on the signature and quality score).
Regarding claim 8, all limitations of claim 1 have been discussed above, FARROKHABADI in view of Rogers discloses comprising the data processing system to: identify a group identifier linked with the plurality of edge devices (FARROKHABADI ¶ 45 The context signature may represent the environment of an energy grid based on the characteristics defined by the context parameters. Context manager 510 then communicates to the edge subsystem 502 a readiness to commence receipt of machine learning input variables and to implement machine learning within the context data structure or model);
push an update to the plurality of edge devices linked with the group identifier (FARROKHABADI ¶ 31 In an embodiment, the system 100 also includes a context manager subsystem 110…, The Context Manager Subsystem 110 is also configured to, on a periodic basis, load or upgrade one or more machine learning models in one or more of the DCC subsystems 102 based on the signature and quality score).
Claims 2, 3, and 15 are rejected under 35 U.S.C. 103 as being unpatentable over FARROKHABADI in view of Rogers (see citation above) further in view of D.I. Dogaru and I. Dumitrache, "Cyber Security of Smart Grids in the Context of Big Data and Machine Learning," 2019 22nd International Conference on Control Systems and Computer Science (CSCS), Bucharest, Romania, 2019, pp. 61-67, doi: 10.1109/CSCS.2019.00018
Regarding claims 2 and 15, the limitations of claims 1 and 14 have been discussed above. FARROKHABADI in view of Rogers discloses comprising the data processing system to: determine a data security policy based on the configuration; and set the application
FARROKHABADI in view of Rogers does not disclose expressly to: determine a data security policy based on the configuration; and set the application with the data security policy, wherein the data security policy controls access to a type of data, a type of metric, or a type of function.
D. I. Dogaru and I. Dumitrache discloses to: determine a data security policy based on the configuration (pg. 65 col.2 In. 1-25 discuss "a machine learning model that classify/identify third party access to sensitive data and clustering which can determine abnormal behavior to determine consequences to the grid." Identifying third party access is interpreted as determining access or a security policy on who can access the data. Clustering is interpreted as detecting bad actors and taking action); and set the application with the data security policy, wherein the data security policy controls access to a type of data, a type of metric, or a type of function (pg. 65 col.2 In. 1-25 discuss "Clustering being classless and being able to study historical communication data to determine abnormal behaviors and dimensionally reduction which is able to take in all the unlabeled data and eliminate variable that have low correlation" Classification, Clustering and reduction can be used to determine a security policy model that is able to control access to any type of data, metric, or function associated with the grid).
FARROKHABADI and Rogers are analogous art because they are from the same field of endeavor edge computing, handling applications and machine learning models.
At the time of the invention, it would have been prima facie obvious to one of ordinary skill, in the art as of the effective filing date, to modify FARROKHABADI to identify an application that contains multiple models for photovoltaic detection that can select a model from the plurality of models responsive to the identification of the application according to Rogers.
The suggestion/motivation for doing so would have been to increase the efficiency of the edge devices by reducing the latency (Rogers ¶20 discloses “Running inferencing applications at edge locations can have various advantages for customers-such as reduced latency-as the edge locations can be closer to the connected media sources and sensors, which can be important for latency-critical applications or services that may be accessed from a variety of different geographic locations.”).
FARROKHABADI in view of Rogers and D. I. DOGARU AND I. DUMITRACHE, are analogous art because they are from same field of endeavor Machine learning on the power grid.
At the time of the invention, it would have been prima facie obvious to one of ordinary skill, in the art as of the effective filing date, to take the security model from D. I. DOGARU AND I. DUMITRACHE and determine a configurable security policy to control access to sensitive data on the edge devices.
The suggestion/motivation for doing so would have been D. I. Dogaru and I. Dumitrache discloses "pg. 64 col. 1, Machine learning algorithms upon security data can detect or predict potential threats with an improved precising over time. Moreover, by analyzing historical data, the algorithms can identify the most frequently targeted entry points or components of the power grid classifying them as "weak-links The cyber security strategies usually imply to minimize the effect of cyber-attacks, because it is a more achievable objective in comparison to completely avoid any type of attack".
Therefore, It would have been prima facie obvious to one of ordinary skill, in the art as of the effective filing date, to combine D. I. DOGARU AND I. DUMITRACHE and FARROKHABADI in view of Rogers for the benefit of determine a data security policy based on the configuration; and set the application with the data security policy, wherein the data security policy controls access to a type of data, a type of metric, or a type of function to obtain the invention as specified in the claims 2 and 15.
Regarding claim 3, the limitations of claim 2 have been discussed above. FARROKHABADI in view of rogers discloses wherein the application filters data transmitted to the data processing system using the data security policy.
FARROKHABADI in view of Rogers does not disclose expressly wherein the application filters data transmitted to the data processing system using the data security policy.
D. I. DOGARU AND I. DUMITRACHE discloses wherein the application filters data transmitted to the data processing system using the data security policy (pg. 65 col.2 In. 1-25 discuss "Dimensionally reduction which is able to take in all the unlabeled data and eliminate variable that have low correlation" Reduction can be used to determine a security policy model that is able filter transmitted
data associated with the grid).
FARROKHABADI and Rogers are analogous art because they are from the same field of endeavor edge computing, handling applications and machine learning models.
At the time of the invention, it would have been prima facie obvious to one of ordinary skill, in the art as of the effective filing date, to modify FARROKHABADI to identify an application that contains multiple models for photovoltaic detection that can select a model from the plurality of models responsive to the identification of the application according to Rogers.
The suggestion/motivation for doing so would have been to increase the efficiency of the edge devices by reducing the latency (Rogers ¶20 discloses “Running inferencing applications at edge locations can have various advantages for customers-such as reduced latency-as the edge locations can be closer to the connected media sources and sensors, which can be important for latency-critical applications or services that may be accessed from a variety of different geographic locations.”).
FARROKHABADI in view of Rogers and D. I. DOGARU AND I. DUMITRACHE, are analogous art because they are from same field of endeavor Machine learning on the power grid.
At the time of the invention, it would have been prima facie obvious to one of ordinary skill, in the art as of the effective filing date, to take the security model from D. I. DOGARU AND I. DUMITRACHE and filter data transmitted to the data processing system by applying the dimensionality reduction policy to remove any unwanted data.
The suggestion/motivation for doing so would have been D. I. Dogaru and I. Dumitrache discloses "pg. 64 col. 1, Machine learning algorithms upon security data can detect or predict potential threats with an improved precising over time. Moreover, by analyzing historical data, the algorithms can identify the most frequently targeted entry points or components of the power grid classifying them as "weak-links" The cyber security strategies usually imply to minimize the effect of cyber-attacks, because it is a more achievable objective in comparison to completely avoid any type of attack".
Therefore, it would have been prima facie obvious to one of ordinary skill, in the art as of the effective filing date, to combine D. I. DOGARU AND I. DUMITRACHE and FARROKHABADI in view of Rogers for the benefit of wherein the application filters data transmitted to the data processing system using the data security policy to obtain the invention as specified in the claim 3.
Claims 9, 11, 13 and 19 and are rejected under 35 U.S.C. 103 as being unpatentable over FARROKHABADI in view of Rogers (see citation above) further in view of BALAKRISHNAN (USPGPUB US20230177349 filed on 10/24/2022)
Regarding claims 9 and 19, the limitations of claims 1 and 14 have been discussed above. FARROKHABADI in view of Rogers discloses determine, based on a computational load balancing policy, to install a second application on a subset of the plurality of edge devices; and provide the second application on the subset of the plurality of edge devices (FARROKHABADI ¶ 31 In an embodiment, the system 100 also includes a context manager subsystem 110 that includes a memory (not shown) and a processor configured to execute instructions stored within the memory to cause the system 110 to maintain a database of one or more machine learning models, associated signatures developed by the context signature subsystem 108, and the quality score of these signatures. The Context Manager Subsystem 110 is also configured to, on a periodic basis, load or upgrade one or more machine learning models in one or more of the DCC subsystems 102 based on the signature and quality score. In this manner, a machine learning model can be applied to a different DCC subsystem 102 based on the model developed for a separate DCC subsystem 102 with similar characteristics, thus reducing the amount of time for training or implementing a machine learning model on a new or different DCC subsystem 102).
FARROKHABADI does not disclose expressly based on a computational load balancing policy, to install a second application on a subset of the plurality of edge devices;
BALAKRISHNAN discloses determine, based on a computational load balancing policy, to install a second application on a subset of the plurality of edge devices (¶ 51-59 contains "pods" which are analogous to applications with models as they are containers that have executable code on them meant to be deployed remotely. The controller decides if the pod is able to be deployed/installed based on the current abilities of the edge device and may choose to relocate it ¶ 51" an edge computing system is extended to provide for orchestration of multiple applications through the use of containers (a contained, deployable unit of software that provides code and needed dependencies)" ¶53 "an edge computing system is extended to provide for orchestration of multiple applications through the use of containers (a contained, deployable unit of software that provides code and needed dependencies)" ¶ 54 " If each tenant specific pod has a tenant specific pod controller, there will be a shared pod controller that consolidates resource allocation requests to avoid potential resource starvation situations.”);
and provide the second application on the subset of the plurality of edge devices (¶ 54 "the orchestrator 460 may provision an attestation verification policy to local pod controllers that perform attestation verification. Ifan attestation satisfies a policy for a first tenant pod controller but not a second tenant pod controller, then the second pod could be migrated to a different edge node that does satisfy it").
FARROKHABADI and Rogers are analogous art because they are from the same field of endeavor edge computing, handling applications and machine learning models.
At the time of the invention, it would have been prima facie obvious to one of ordinary skill, in the art as of the effective filing date, to modify FARROKHABADI to identify an application that contains multiple models for photovoltaic detection that can select a model from the plurality of models responsive to the identification of the application according to Rogers.
The suggestion/motivation for doing so would have been to increase the efficiency of the edge devices by reducing the latency (Rogers ¶20 discloses “Running inferencing applications at edge locations can have various advantages for customers-such as reduced latency-as the edge locations can be closer to the connected media sources and sensors, which can be important for latency-critical applications or services that may be accessed from a variety of different geographic locations.”).
FARROKHABADI in view of Rogers further in view of BALAKRISHNAN are analogous art because they are from field of endeavor machine learning devices on the power grid.
At the time of the invention, it would have been prima facie obvious to one of ordinary skill, in the art as of the effective filing date, to modify FARROKHABADI in view of Rogers further in view of BALAKRISHNAN to include the ability to manage/install many applications based on the computational load requirements and provide the application.
The suggestion/motivation for doing so would have been BALAKRISHNAN discloses (¶ 31 “Compute, memory, and storage resources which are offered at the edges in the edge cloud 110 are critical to providing ultra-low latency response times for services and functions used by the endpoint data sources 160 as well as reduce network backhaul traffic from the edge cloud 110 toward cloud data center 130 thus improving energy consumption and overall network usages among other benefits").
Therefore, it would have been prima facie obvious to one of ordinary skill, in the art as of the effective filing date, to combine FARROKHABADI in view of Rogers further in view of BALAKRISHNAN for the benefit of determine, based on a computational load balancing policy, to install a second application on a subset of the plurality of edge devices; and provide the second application on the subset of the plurality of edge devices to obtain the invention as specified in the claims 9 and 19.
Regarding claim 11, the limitations of claim 1 have been discussed above. FARROKHABADI in view of Rogers discloses the system, comprising the data processing system to: receive the application from a third-party application developer device remote from the data processing system.
FARROKHABADI in view of Rogers does not expressly disclose to: receive the application from a third-party application developer device remote from the data processing system.
BALAKRISHNAN discloses to: receive the application from a third-party application developer device remote from the data processing system (¶ 64-70 disclose a developer being able to post models on a third party site that a user can access/pay for to receive and download the models to their edge device. ¶ 65 "a developer writes function code (e.g., "computer code" herein) representing one or more computer functions, and the function code is uploaded to a FaaS platform provided by, for example, an edge node or data center." ¶ 68 "The receiving parties may be consumers, service providers, users, retailers, OEMs, etc., who purchase and/or license the software instructions for use and/or re- sale and/or sub-licensing." ¶ 69 "The servers enable purchasers and/or licensors to download the computer readable instructions 882 from the edge provisioning node").
FARROKHABADI and Rogers are analogous art because they are from the same field of endeavor edge computing, handling applications and machine learning models.
At the time of the invention, it would have been prima facie obvious to one of ordinary skill, in the art as of the effective filing date, to modify FARROKHABADI to identify an application that contains multiple models for photovoltaic detection that can select a model from the plurality of models responsive to the identification of the application according to Rogers.
The suggestion/motivation for doing so would have been to increase the efficiency of the edge devices by reducing the latency (Rogers ¶20 discloses “Running inferencing applications at edge locations can have various advantages for customers-such as reduced latency-as the edge locations can be closer to the connected media sources and sensors, which can be important for latency-critical applications or services that may be accessed from a variety of different geographic locations.”).
FARROKHABADI in view of Rogers further in view of BALAKRISHNAN are analogous art because they are from field of endeavor machine learning devices on the power grid.
At the time of the invention, it would have been prima facie obvious to one of ordinary skill, in the art as of the effective filing date, to modify FARROKHABADI in view of Rogers further in view of BALAKRISHNAN to receive the application from a third-party application developer device remote from the data processing system.
The suggestion/motivation for doing so would have been BALAKRISHNAN discloses ("1/31 Compute, memory, and storage resources which are offered at the edges in the edge cloud 110 are critical to providing ultra-low latency response times for services and functions used by the endpoint data sources 160 as well as reduce network backhaul traffic from the edge cloud 110 toward cloud data center 130 thus improving energy consumption and overall network usages among other benefits").
Therefore, it would have been prima facie obvious to one of ordinary skill, in the art as of the effective filing date, to combine FARROKHABADI in view of Rogers further in view of BALAKRISHNAN for the benefit of to: receive the application from a third-party application developer device remote from the data processing system. to obtain the invention as specified in the claim 11.
Regarding claim 13 the limitations of claim 1 have been discussed above. FARROKHABADI in view of Rogers discloses to: provide a runtime environment on the data processing system to host a second application configured to interface with the application executed on the plurality of edge devices.
FARROKHABADI in view of Rogers does not expressly disclose to: provide a runtime environment on the data processing system to host a second application configured to interface with the application executed on the plurality of edge devices.
BALAKRISHNAN discloses provide a runtime environment on the data processing system to host a second application configured to interface with the application executed on the plurality of edge devices (¶65-66 A trigger such as, for example, a service use case or an edge processing event, initiates the execution of the function code with the FaaS platform. [0066] In an example of FaaS, a container is used to provide an environment in which function code (e.g., an application which may be provided by a third party) is executed. The container may be any isolated-execution entity such as a process, a Docker or Kubernetes container, a virtual machine, etc. Within the edge computing system, various datacenter, edge, and endpoint (including mobile) devices are used to "spin up" functions (e.g., activate and/or allocate function actions) that are scaled on demand ¶51 For instance, an edge computing system may be configured to fulfill requests and responses for various client endpoints from multiple virtual edge instances (and, from a cloud or remote data center). The use of these virtual edge instances may support multiple tenants and multiple applications).
FARROKHABADI and Rogers are analogous art because they are from the same field of endeavor edge computing, handling applications and machine learning models.
At the time of the invention, it would have been prima facie obvious to one of ordinary skill, in the art as of the effective filing date, to modify FARROKHABADI to identify an application that contains multiple models for photovoltaic detection that can select a model from the plurality of models responsive to the identification of the application according to Rogers.
The suggestion/motivation for doing so would have been to increase the efficiency of the edge devices by reducing the latency (Rogers ¶20 discloses “Running inferencing applications at edge locations can have various advantages for customers-such as reduced latency-as the edge locations can be closer to the connected media sources and sensors, which can be important for latency-critical applications or services that may be accessed from a variety of different geographic locations.”).
FARROKHABADI in view of Rogers further in view of BALAKRISHNAN are analogous art because they are from field of endeavor machine learning devices on the power grid.
At the time of the invention, it would have been prima facie obvious to one of ordinary skill, in the art as of the effective filing date, to modify FARROKHABADI in view of Rogers further in view of BALAKRISHNAN to: provide a runtime environment on the data processing system to host a second application configured to interface with the application executed on the plurality of edge devices.
The suggestion/motivation for doing so would have been BALAKRISHNAN discloses ("¶ 31 Compute, memory, and storage resources which are offered at the edges in the edge cloud 110 are critical to providing ultra-low latency response times for services and functions used by the endpoint data sources 160 as well as reduce network backhaul traffic from the edge cloud 110 toward cloud data center 130 thus improving energy consumption and overall network usages among other benefits").
Therefore, it would have been prima facie obvious to one of ordinary skill, in the art as of the effective filing date, to combine FARROKHABADI in view of Rogers further in view of BALAKRISHNAN for the benefit of to: provide a runtime environment on the data processing system to host a second application configured to interface with the application executed on the plurality of edge devices to obtain the invention as specified in the claim 13.
Claim 10 is rejected under 35 U.S.C. 103 as being unpatentable over FARROKHABADI in view of Rogers (see citation above) further in view of DEAVER (USPGPUB US20090184835 published on 07/23/2009).
Regarding claim 10, limitations of claim 1 have been discussed above. FARROKHABADI in view of Rogers discloses wherein the application determines a distance to a fault responsive to detection of a power outage notification.
FARROKHABADI in view of Rogers does not disclose expressly determines a distance to a fault responsive to detection of a power outage notification.
DEAVER discloses determines a distance to a fault responsive to detection of a power outage notification (¶ 96 "when the remote computer (e.g., a power line server) receives the notifications from one or more PLCDs137, it may determine the location of the outage and map the power outage on a map that is presented on a display (including streets and power lines thereon) to allow utility personnel to easily identify the location of the fault (and what protection device(s) may have tripped and need attention). As discussed above, upon powering up the access devices139 may access their non-volatile memory to determine if a flag bit is stored therein and if so, to transmit a live alert indicating that (1) the device is back on the network and (2) the device shut down because of a power outage") One skilled in the art having the location of the fault and being notified of the outage would be able to predictably calculate the distance to the fault.
FARROKHABADI and Rogers are analogous art because they are from the same field of endeavor edge computing, handling applications and machine learning models.
At the time of the invention, it would have been prima facie obvious to one of ordinary skill, in the art as of the effective filing date, to modify FARROKHABADI to identify an application that contains multiple models for photovoltaic detection that can select a model from the plurality of models responsive to the identification of the application according to Rogers.
The suggestion/motivation for doing so would have been to increase the efficiency of the edge devices by reducing the latency (Rogers ¶20 discloses “Running inferencing applications at edge locations can have various advantages for customers-such as reduced latency-as the edge locations can be closer to the connected media sources and sensors, which can be important for latency-critical applications or services that may be accessed from a variety of different geographic locations.”).
FARROKHABADI in view of Rogers and DEAVER are analogous art because they are from same field of endeavor Fault detection on a power grid.
At the time of the invention, it would have been prima facie obvious to one of ordinary skill, in the art as of the effective filing date, to modify FARROKHABADI in view of Rogers further in view of DEAVER so that the application determines a distance to a fault responsive to detection of a power outage notification.
The suggestion/motivation for doing so would have been DEAVER discloses (¶ 4 "It is desirable that the utility operator quickly identify and respond to such power distribution events to minimize the adverse impact to the power distribution system and to the consumers. In particular, it is desirable to determine what adverse power distribution event may occur (or has occurred) and the location of such an event").
Therefore, it would have been prima facie obvious to one of ordinary skill, in the art as of the effective filing date, to combine FARROKHABADI in view of Rogers further in view of DEAVER for the benefit of the application determines a distance to a fault responsive to detection of a power outage notification to obtain the invention as specified in the claim 10.
Allowable Subject Matter
Claim 12 and 20 objected to as being dependent upon a rejected base claim, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims.
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
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/C.D.C./Examiner, Art Unit 2115
/PAUL B YANCHUS III/ Primary Examiner, Art Unit 2115 September 2, 2026
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